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Journal of Energy in Southern Africa

versión On-line ISSN 2413-3051
versión impresa ISSN 1021-447X


GHAYOOR, Farzad; SWANSON, Andrew G.  y  SIBANDA, Hudson. Optimal sizing for a grid-connected hybrid renewable energy system: A case study of the residential sector in Durban, South Africa. J. energy South. Afr. [online]. 2021, vol.32, n.4, pp.11-27. ISSN 2413-3051.

Many countries, including South Africa, have introduced policies and incentives to increase their renewable energy capacities in order to address environmental concerns and reduce pollutant emissions into the atmosphere. In addition, consumers in South Africa have faced the ever-increasing price of electricity and unreliability of the grid since 2007 due to the lack of sufficient electricity production. As a result, employing hybrid renewable energy systems (HRESs) have gained popularity. This research focuses on grid-connected HRESs based on solar photovoltaic (PV) panels and wind turbines as a potential way of reducing the dependency of residential sector consumers on the grid. It aims to identify the optimal sizing of renewable energy sources to be cost-effective for consumers over a certain period of time, using Durban as a case study. Two artificial intelligence methods have been used to obtain the optimal sizing for the available PV panels, wind turbines and inverters. The results shown that the combination of PV panels and battery storage can be a profitable option. A system using higher rated power PV panels can start to become profitable in a shorter lifetime, but employing batteries can only be cost-effective if a long enough lifetime is considered.HIGHLIGHTS: • Modelling the load and a HRES based on the residential consumer needs and available products. • Defining the optimisation problem based on a cost evaluation indicator and identifying constraints. • Determining the optimum combination of renewable energy sources. • Assessing the cost of setting up a HRES for typical residential consumers.

Palabras clave : cost-effective HRES; genetic algorithm optimiser; particle swarm optimiser; wind and solar hybrid energy systems.

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